Robust ICA for Super-Gaussian Sources
2004
Conference Paper
ei
Most ICA algorithms are sensitive to outliers. Instead of robustifying existing algorithms by outlier rejection techniques, we show how a simple outlier index can be used directly to solve the ICA problem for super-Gaussian source signals. This ICA method is outlier-robust by construction and can be used for standard ICA as well as for over-complete ICA (i.e. more source signals than observed signals (mixtures)).
Author(s): | Meinecke, F. and Harmeling, S. and Müller, K-R. |
Book Title: | ICA 2004 |
Journal: | Independent Component Analysis and Blind Signal Separation: Fifth International Conference (ICA 2004) |
Pages: | 217-224 |
Year: | 2004 |
Month: | October |
Day: | 0 |
Editors: | Puntonet, C. G., A. Prieto |
Publisher: | Springer |
Department(s): | Empirische Inferenz |
Bibtex Type: | Conference Paper (inproceedings) |
DOI: | 10.1007/b100528 |
Event Name: | Fifth International Conference on Independent Component Analysis and Blind Signal Separation |
Event Place: | Granada, Spain |
Address: | Berlin, Germany |
Digital: | 0 |
Language: | en |
Organization: | Max-Planck-Gesellschaft |
School: | Biologische Kybernetik |
BibTex @inproceedings{6352, title = {Robust ICA for Super-Gaussian Sources}, author = {Meinecke, F. and Harmeling, S. and M{\"u}ller, K-R.}, journal = {Independent Component Analysis and Blind Signal Separation: Fifth International Conference (ICA 2004)}, booktitle = {ICA 2004}, pages = {217-224}, editors = {Puntonet, C. G., A. Prieto}, publisher = {Springer}, organization = {Max-Planck-Gesellschaft}, school = {Biologische Kybernetik}, address = {Berlin, Germany}, month = oct, year = {2004}, doi = {10.1007/b100528}, month_numeric = {10} } |